Online adaptive lossless compression with lockstep-trained predictive models

Abstract

A method and system are disclosed for adaptive lossless data compression using a predictive model that is updated deterministically in lockstep at both encoder and decoder. An encoder generates symbol probability distributions from a frozen base model with a small set of updatable parameters, encodes input blocks via entropy coding, and applies deterministic parameter updates using the observed data. A decoder entropy-decodes the compressed bitstream, reconstructs the same blocks, and applies identical updates, thereby maintaining synchronization without transmission of model parameters. Optional features include periodic beacons for state verification and resynchronization, error-control metadata, and modality-specific tokenization. In preferred embodiments, parameter adaptation is confined to adapter modules within a largely fixed neural network, ensuring computational efficiency and reproducibility across platforms. Over time, the model specializes to the stream, reducing average coding rate while guaranteeing exact reconstruction of the original data.

1 . A method of lossless data compression comprising:
 receiving, by an encoder, an input block of symbols from a data stream;   generating, by a predictive model comprising a frozen base model and an updatable parameter subset, probability distributions for symbols of the input block;   entropy encoding, by an entropy coder, the input block into a codeword using the probability distributions;   transmitting the codeword as part of a compressed bitstream over a channel to a decoder;   updating, by the encoder, the updatable parameter subset according to a deterministic update rule using the input block as training data;   decoding, by the decoder, the compressed bitstream using the predictive model to reconstruct the input block by entropy decoding; and   updating, by the decoder, the updatable parameter subset according to the same deterministic update rule using the reconstructed block, wherein synchronization between encoder and decoder is maintained without transmission of model parameters.   
     
     
         2 . The method of  claim 1 , wherein the updatable parameter subset comprises adapter modules inserted into a largely frozen neural network. 
     
     
         3 . The method of  claim 1 , wherein the deterministic update rule is executed by an optimizer whose state is derivable from public hyperparameters, block indices, and observed data. 
     
     
         4 . The method of  claim 1 , further comprising inserting, by the encoder, periodic synchronization beacons into the compressed bitstream, each beacon encoding a compact representation of the updated model state, and verifying, by the decoder, each beacon against a locally computed value. 
     
     
         5 . The method of  claim 4 , further comprising initiating a resynchronization procedure upon detecting a beacon mismatch, the resynchronization procedure comprising one of: rolling back the predictive model to a previously verified state or applying a corrective delta transmitted from the encoder. 
     
     
         6 . The method of  claim 1 , wherein the entropy coder is selected from the group consisting of arithmetic coding, range coding, rANS coding, and table-based tANS coding, and wherein coding precision and renormalization parameters are fixed to ensure reproducibility. 
     
     
         7 . The method of  claim 1 , wherein numerical determinism is enforced by constraining computation kernels, fixing random number generator seeds, and applying explicit rounding rules such that identical inputs and hyperparameters yield identical outputs at encoder and decoder across different platforms. 
     
     
         8 . The method of  claim 1 , wherein the compressed bitstream further comprises integrity metadata including per-block checksums or forward error correction information. 
     
     
         9 . The method of  claim 1 , wherein tokenization of the input block is modality-specific and selected from the group consisting of byte-level tokenization, subword tokenization, image patch tokenization, quantized residual tokenization, and audio latent representation. 
     
     
         10 . The method of  claim 1 , wherein the predictive model is initialized from a pretrained checkpoint and is progressively specialized to the input stream through successive deterministic updates, thereby reducing average coding rate in bits per symbol over time. 
     
     
         11 . A system for lossless data compression comprising:
 an encoder including a predictive model with an updatable parameter subset and an entropy coder, the encoder configured to encode input data into a compressed bitstream and to apply deterministic updates to the updatable parameter subset;   a decoder including a corresponding predictive model and an entropy decoding module, the decoder configured to decode the compressed bitstream and to apply identical deterministic updates to the updatable parameter subset; and   a synchronization mechanism comprising beacon signals and optional resynchronization procedures, whereby the encoder and decoder remain in lockstep without transmission of model parameters.   
     
     
         12 . The system of  claim 11 , wherein the encoder further comprises a dispatcher configured to distribute data packets to multiple compressor processes each associated with a distinct predictive model, and a transmitter configured to select compressed outputs from the compressor processes and transmit them together with compression metadata. 
     
     
         13 . The system of  claim 11 , wherein the decoder comprises multiple decompressor processes configured to reconstruct data packets using predictive models corresponding to the models used by the encoder. 
     
     
         14 . The system of  claim 11 , wherein the predictive model includes adapter modules with updatable parameters updated deterministically via an optimizer along an update path, with optimizer state derivable from public hyperparameters. 
     
     
         15 . The system of  claim 11 , wherein the system is configured to reduce average bits-per-symbol coding rate as more data is processed, yielding an adaptive coding efficiency trajectory lower than that of a static predictor. 
     
     
         16 . The system of  claim 11 , wherein the synchronization mechanism further comprises validation modules that compare received beacon values against historical parameter states. 
     
     
         17 . The method of  claim 1 , further comprising applying auxiliary processes for model fine-tuning during long-running streams, wherein fine-tuned models replace original models after synchronization with the decoder. 
     
     
         18 . The system of  claim 11 , wherein auxiliary processes provide redundancy or load balancing across multiple compressor or decompressor modules. 
     
     
         19 . The method of  claim 1 , wherein error-control modules are integrated into the pipeline to perform checksum verification, forward error correction, or adaptive buffering during compression and decompression. 
     
     
         20 . The system of  claim 11 , wherein best-mode operation comprises implementing the predictive model as a Transformer architecture pretrained on a mixed corpus of text and binary data, updating adapter modules with a stochastic gradient descent optimizer using a fixed learning rate and predetermined block size.